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Rebranding from Cognitive Search to AI Search.
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gmndrg authored Nov 15, 2023
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# "Chat with your data" Solution Accelerator

> [!IMPORTANT]
> As of November 15, 2023, Azure Cognitive Search has been renamed to Azure AI Search.
Welcome to the "Chat with your data" Solution Accelerator repository!
The "Chat with your data" Solution Accelerator is a powerful tool that combines the capabilities of Azure Cognitive Search and Large Language Models (LLMs) to create a conversational search experience. This solution accelerator uses an Azure OpenAI GPT model and an Azure Cognitive Search index generated from your data, which is integrated into a web application to provide a natural language interface for search queries.
The "Chat with your data" Solution Accelerator is a powerful tool that combines the capabilities of Azure AI Search and Large Language Models (LLMs) to create a conversational search experience. This solution accelerator uses an Azure OpenAI GPT model and an Azure AI Search index generated from your data, which is integrated into a web application to provide a natural language interface for search queries.

![A screenshot of the accelerator architecture.](media/architecture.png)

Expand All @@ -27,16 +30,16 @@ The accelerator presented here provides several options, for example:
* An admin site for ingesting/inspecting/configuring your dataset on the fly
* Running a Retrieval Augmented Generation (RAG) solution locally, as a Docker container

*Have you seen [ChatGPT + Enterprise data with Azure OpenAI and Cognitive Search demo](https://github.com/Azure-Samples/azure-search-openai-demo)? If you would like to experiment: Play with prompts, understanding RAG pattern different implementation approaches, see how different features interact with the RAG pattern and choose the best options for your RAG deployments, take a look at that repo.
*Have you seen [ChatGPT + Enterprise data with Azure OpenAI and AI Search demo](https://github.com/Azure-Samples/azure-search-openai-demo)? If you would like to experiment: Play with prompts, understanding RAG pattern different implementation approaches, see how different features interact with the RAG pattern and choose the best options for your RAG deployments, take a look at that repo.

Here is a comparison table with a few features offered by Azure, an available GitHub demo sample and this repo, that can provide guidance when you need to decide which one to use:

| Name | Feature or Sample? | What is it? | When to use? |
| ---------|---------|---------|---------|
|[Azure OpenAI on your data](https://learn.microsoft.com/azure/ai-services/openai/concepts/use-your-data) | Azure feature | Azure OpenAI Service offers out-of-the-box, end-to-end RAG implementation that uses a REST API or the web-based interface in the Azure AI Studio to create a solution that connects to your data to enable an enhanced chat experience with Azure OpenAI ChatGPT models and Azure Cognitive Search. | This should be the first option considered for developers that need an end-to-end solution for Azure OpenAI Service with an Azure Cognitive Search retriever. Simply select supported data sources, that ChatGPT model in Azure OpenAI Service , and any other Azure resources needed to configure your enterprise application needs. |
|[Azure Machine Learning prompt flow](https://learn.microsoft.com/azure/machine-learning/concept-retrieval-augmented-generation) | Azure feature | RAG in Azure Machine Learning is enabled by integration with Azure OpenAI Service for large language models and vectorization. It includes support for Faiss and Azure Cognitive Search as vector stores, as well as support for open-source offerings, tools, and frameworks such as LangChain for data chunking. Azure Machine Learning prompt flow offers the ability to test data generation, automate prompt creation, visualize prompt evaluation metrics, and integrate RAG workflows into MLOps using pipelines. | When Developers need more control over processes involved in the development cycle of LLM-based AI applications, they should use Azure Machine Learning prompt flow to create executable flows and evaluate performance through large-scale testing. |
|["Chat with your data" Solution Accelerator](https://aka.ms/ChatWithYourDataSolutionAccelerator) - (This repo) | Azure sample | End-to-end baseline RAG pattern sample that uses Azure Cognitive Search as a retriever. | This sample should be used by Developers when the RAG pattern implementations provided by Azure are not able to satisfy business requirements. This sample provides a means to customize the solution. Developers must add their own code to meet requirements, and adapt with best practices according to individual company policies. |
|[ChatGPT + Enterprise data with Azure OpenAI and Cognitive Search demo](https://github.com/Azure-Samples/azure-search-openai-demo) | Azure sample | RAG pattern demo that uses Azure Cognitive Search as a retriever. | Developers who would like to use or present an end-to-end demonstration of the RAG pattern should use this sample. This includes the ability to deploy and test different retrieval modes, and prompts to support business use cases. |
|[Azure OpenAI on your data](https://learn.microsoft.com/azure/ai-services/openai/concepts/use-your-data) | Azure feature | Azure OpenAI Service offers out-of-the-box, end-to-end RAG implementation that uses a REST API or the web-based interface in the Azure AI Studio to create a solution that connects to your data to enable an enhanced chat experience with Azure OpenAI ChatGPT models and Azure AI Search. | This should be the first option considered for developers that need an end-to-end solution for Azure OpenAI Service with an Azure AI Search retriever. Simply select supported data sources, that ChatGPT model in Azure OpenAI Service , and any other Azure resources needed to configure your enterprise application needs. |
|[Azure Machine Learning prompt flow](https://learn.microsoft.com/azure/machine-learning/concept-retrieval-augmented-generation) | Azure feature | RAG in Azure Machine Learning is enabled by integration with Azure OpenAI Service for large language models and vectorization. It includes support for Faiss and Azure AI Search as vector stores, as well as support for open-source offerings, tools, and frameworks such as LangChain for data chunking. Azure Machine Learning prompt flow offers the ability to test data generation, automate prompt creation, visualize prompt evaluation metrics, and integrate RAG workflows into MLOps using pipelines. | When Developers need more control over processes involved in the development cycle of LLM-based AI applications, they should use Azure Machine Learning prompt flow to create executable flows and evaluate performance through large-scale testing. |
|["Chat with your data" Solution Accelerator](https://aka.ms/ChatWithYourDataSolutionAccelerator) - (This repo) | Azure sample | End-to-end baseline RAG pattern sample that uses Azure AI Search as a retriever. | This sample should be used by Developers when the RAG pattern implementations provided by Azure are not able to satisfy business requirements. This sample provides a means to customize the solution. Developers must add their own code to meet requirements, and adapt with best practices according to individual company policies. |
|[ChatGPT + Enterprise data with Azure OpenAI and AI Search demo](https://github.com/Azure-Samples/azure-search-openai-demo) | Azure sample | RAG pattern demo that uses Azure AI Search as a retriever. | Developers who would like to use or present an end-to-end demonstration of the RAG pattern should use this sample. This includes the ability to deploy and test different retrieval modes, and prompts to support business use cases. |


## Supported file types
Expand Down Expand Up @@ -213,22 +216,22 @@ docker push YOUR_DOCKER_REGISTRY/YOUR_DOCKER_IMAGE

| App Setting | Value | Note |
| --- | --- | ------------- |
|AZURE_SEARCH_SERVICE||The URL of your Azure Cognitive Search resource. e.g. https://<search-service>.search.windows.net|
|AZURE_SEARCH_INDEX||The name of your Azure Cognitive Search Index|
|AZURE_SEARCH_KEY||An **admin key** for your Azure Cognitive Search resource|
|AZURE_SEARCH_SERVICE||The URL of your Azure AI Search resource. e.g. https://<search-service>.search.windows.net|
|AZURE_SEARCH_INDEX||The name of your Azure AI Search Index|
|AZURE_SEARCH_KEY||An **admin key** for your Azure AI Search resource|
|AZURE_SEARCH_USE_SEMANTIC_SEARCH|False|Whether or not to use semantic search|
|AZURE_SEARCH_SEMANTIC_SEARCH_CONFIG||The name of the semantic search configuration to use if using semantic search.|
|AZURE_SEARCH_TOP_K|5|The number of documents to retrieve from Azure Cognitive Search.|
|AZURE_SEARCH_TOP_K|5|The number of documents to retrieve from Azure AI Search.|
|AZURE_SEARCH_ENABLE_IN_DOMAIN|True|Limits responses to only queries relating to your data.|
|AZURE_SEARCH_CONTENT_COLUMNS||List of fields in your Azure Cognitive Search index that contains the text content of your documents to use when formulating a bot response. Represent these as a string joined with "|", e.g. `"product_description|product_manual"`|
|AZURE_SEARCH_CONTENT_VECTOR_COLUMNS||Field from your Azure Cognitive Search index for storing the content's Vector embeddings|
|AZURE_SEARCH_CONTENT_COLUMNS||List of fields in your Azure AI Search index that contains the text content of your documents to use when formulating a bot response. Represent these as a string joined with "|", e.g. `"product_description|product_manual"`|
|AZURE_SEARCH_CONTENT_VECTOR_COLUMNS||Field from your Azure AI Search index for storing the content's Vector embeddings|
|AZURE_SEARCH_DIMENSIONS|1536| Azure OpenAI Embeddings dimensions. 1536 for `text-embedding-ada-002`|
|AZURE_SEARCH_FIELDS_ID|id|`AZURE_SEARCH_FIELDS_ID`: Field from your Azure Cognitive Search index that gives a unique idenitfier of the document chunk. `id` if you don't have a specific requirement.|
|AZURE_SEARCH_FILENAME_COLUMN||`AZURE_SEARCH_FILENAME_COLUMN`: Field from your Azure Cognitive Search index that gives a unique idenitfier of the source of your data to display in the UI.|
|AZURE_SEARCH_TITLE_COLUMN||Field from your Azure Cognitive Search index that gives a relevant title or header for your data content to display in the UI.|
|AZURE_SEARCH_URL_COLUMN||Field from your Azure Cognitive Search index that contains a URL for the document, e.g. an Azure Blob Storage URI. This value is not currently used.|
|AZURE_SEARCH_FIELDS_TAG|tag|Field from your Azure Cognitive Search index that contains tags for the document. `tag` if you don't have a specific requirement.|
|AZURE_SEARCH_FIELDS_METADATA|metadata|Field from your Azure Cognitive Search index that contains metadata for the document. `metadata` if you don't have a specific requirement.|
|AZURE_SEARCH_FIELDS_ID|id|`AZURE_SEARCH_FIELDS_ID`: Field from your Azure AI Search index that gives a unique idenitfier of the document chunk. `id` if you don't have a specific requirement.|
|AZURE_SEARCH_FILENAME_COLUMN||`AZURE_SEARCH_FILENAME_COLUMN`: Field from your Azure AI Search index that gives a unique idenitfier of the source of your data to display in the UI.|
|AZURE_SEARCH_TITLE_COLUMN||Field from your Azure AI Search index that gives a relevant title or header for your data content to display in the UI.|
|AZURE_SEARCH_URL_COLUMN||Field from your Azure AI Search index that contains a URL for the document, e.g. an Azure Blob Storage URI. This value is not currently used.|
|AZURE_SEARCH_FIELDS_TAG|tag|Field from your Azure AI Search index that contains tags for the document. `tag` if you don't have a specific requirement.|
|AZURE_SEARCH_FIELDS_METADATA|metadata|Field from your Azure AI Search index that contains metadata for the document. `metadata` if you don't have a specific requirement.|
|AZURE_OPENAI_RESOURCE||the name of your Azure OpenAI resource|
|AZURE_OPENAI_MODEL||The name of your model deployment|
|AZURE_OPENAI_MODEL_NAME|gpt-35-turbo|The name of the model|
Expand Down Expand Up @@ -258,7 +261,7 @@ docker push YOUR_DOCKER_REGISTRY/YOUR_DOCKER_IMAGE

This solution accelerator deploys the following resources. It's crucial to comprehend the functionality of each. Below are the links to their respective documentation:
- [Azure OpenAI Service Documentation](https://learn.microsoft.com/azure/ai-services/openai/)
- [Azure Cognitive Search Documentation](https://learn.microsoft.com/azure/search/)
- [Azure AI Search Documentation](https://learn.microsoft.com/azure/search/)
- [Azure Blob Storage Documentation](https://learn.microsoft.com/azure/storage/blobs/)
- [Azure Functions Documentation](https://learn.microsoft.com/azure/azure-functions/)
- [Azure AI Document Intelligence Documentation](https://learn.microsoft.com/azure/ai-services/document-intelligence/)
Expand All @@ -271,26 +274,26 @@ This solution accelerator deploys multiple resources. Evaluate the cost of each

The following are links to the pricing details for some of the resources:
- [Azure OpenAI service pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/). GPT and embedding models are charged separately.
- [Azure Cognitive Search pricing](https://azure.microsoft.com/pricing/details/search/). Cognitive Search core service and semantic search are charged separately.
- [Azure AI Search pricing](https://azure.microsoft.com/pricing/details/search/). AI Search core service and semantic search are charged separately.
- [Azure Blob Storage pricing](https://azure.microsoft.com/pricing/details/storage/blobs/)
- [Azure Functions pricing](https://azure.microsoft.com/pricing/details/functions/)
- [Azure AI Document Intelligence pricing](https://azure.microsoft.com/pricing/details/ai-document-intelligence/)
- [Azure Web App Pricing](https://azure.microsoft.com/pricing/details/app-service/windows/)


## Azure Cognitive Search used as retriever in RAG
## Azure AI Search used as retriever in RAG

Azure Cognitive Search, when used as a retriever in the Retrieval-Augmented Generation (RAG) pattern, plays a key role in fetching relevant information from a large corpus of data. The RAG pattern involves two key steps: retrieval of documents and generation of responses. Azure Cognitive Search, in the retrieval phase, filters and ranks the most relevant documents from the dataset based on a given query.
Azure AI Search, when used as a retriever in the Retrieval-Augmented Generation (RAG) pattern, plays a key role in fetching relevant information from a large corpus of data. The RAG pattern involves two key steps: retrieval of documents and generation of responses. Azure AI Search, in the retrieval phase, filters and ranks the most relevant documents from the dataset based on a given query.

The importance of optimizing data in the index for relevance lies in the fact that the quality of retrieved documents directly impacts the generation phase. The more relevant the retrieved documents are, the more accurate and pertinent the generated responses will be.

Azure Cognitive Search allows for fine-tuning the relevance of search results through features such as [scoring profiles](https://learn.microsoft.com/azure/search/index-add-scoring-profiles), which assign weights to different fields, [Lucene's powerful full-text search capabilities](https://learn.microsoft.com/azure/search/query-lucene-syntax), [vector search](https://learn.microsoft.com/azure/search/vector-search-overview) for similarity search, multi-modal search, recommendations, [hybrid search](https://learn.microsoft.com/azure/search/hybrid-search-overview) and [semantic search](https://learn.microsoft.com/azure/search/search-get-started-semantic) to use AI from Microsoft to rescore search results and moving results that have more semantic relevance to the top of the list. By leveraging these features, one can ensure that the most relevant documents are retrieved first, thereby improving the overall effectiveness of the RAG pattern.
Azure AI Search allows for fine-tuning the relevance of search results through features such as [scoring profiles](https://learn.microsoft.com/azure/search/index-add-scoring-profiles), which assign weights to different fields, [Lucene's powerful full-text search capabilities](https://learn.microsoft.com/azure/search/query-lucene-syntax), [vector search](https://learn.microsoft.com/azure/search/vector-search-overview) for similarity search, multi-modal search, recommendations, [hybrid search](https://learn.microsoft.com/azure/search/hybrid-search-overview) and [semantic search](https://learn.microsoft.com/azure/search/search-get-started-semantic) to use AI from Microsoft to rescore search results and moving results that have more semantic relevance to the top of the list. By leveraging these features, one can ensure that the most relevant documents are retrieved first, thereby improving the overall effectiveness of the RAG pattern.

Moreover, optimizing the data in the index also enhances the efficiency, the speed of the retrieval process and increases relevance which is an integral part of the RAG pattern.



## Best practices specific to Azure Cognitive Search
## Best practices specific to Azure AI Search
- Consider switching security keys and using [RBAC](https://learn.microsoft.com/azure/search/search-security-rbac) instead for authentication.
- Consider setting up a [firewall](https://learn.microsoft.com/azure/search/service-configure-firewall), [private endpoints](https://learn.microsoft.com/azure/search/service-create-private-endpoint) for inbound connections and [shared private links](https://learn.microsoft.com/azure/search/search-indexer-howto-access-trusted-service-exception) for [built-in pull indexers](https://learn.microsoft.com/en-us/azure/search/search-indexer-overview).
- For the best results, prepare your index data and consider [analyzers](https://learn.microsoft.com/azure/search/search-analyzers).
Expand All @@ -300,7 +303,7 @@ Moreover, optimizing the data in the index also enhances the efficiency, the spe

## Best practices before deploying Azure RAG implementations to production
- Follow the best practices described in [Azure Well-Architected-Framework](https://learn.microsoft.com/azure/well-architected/).
- Understand the [Retrieval Augmented Generation (RAG) in Azure Cognitive Search](https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview).
- Understand the [Retrieval Augmented Generation (RAG) in Azure AI Search](https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview).
- Understand the [functionality and configuration that would adapt better to your solution](https://techcommunity.microsoft.com/t5/azure-ai-services-blog/azure-cognitive-search-outperforming-vector-search-with-hybrid/ba-p/3929167) and test with your own data for optimal retrieval.
- Experiment with different options, define the prompts that are optimal for your needs and find ways to implement functionality tailored to your business needs with [this demo](https://github.com/Azure-Samples/azure-search-openai-demo), so you can then adapt to the accelerator.
- Follow the [Responsible AI best practices](https://www.microsoft.com/en-us/ai/tools-practices).
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